Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
162 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

α-HMM: A Graphical Model for RNA Folding (2401.03571v1)

Published 7 Jan 2024 in q-bio.BM and cs.LG

Abstract: RNA secondary structure is modeled with the novel arbitrary-order hidden Markov model ({\alpha}-HMM). The {\alpha}-HMM extends over the traditional HMM with capability to model stochastic events that may be in influenced by historically distant ones, making it suitable to account for long-range canonical base pairings between nucleotides, which constitute the RNA secondary structure. Unlike previous heavy-weight extensions over HMM, the {\alpha}-HMM has the flexibility to apply restrictions on how one event may influence another in stochastic processes, enabling efficient prediction of RNA secondary structure including pseudoknots.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (38)
  1. The trouble with long-range base pairs in rna folding. Lecture Notes in Bioinformatics 8213 (2013), 1–11.
  2. Evolving stochastic context-free grammars for rna secondary structure prediction. BMC Bioinformatics 13, 78 (2012).
  3. Tertiary motifs in rna structure and folding. Angewandte Chemie (International Ed in English) 38, 16 (1999), 2326–2343.
  4. The noncoding rna revolution-trashing old rules to forge new ones. Cell 157, 1 (2014), 77–94.
  5. Clifford, R. Markov random fields in statistics. In Grimmett, G. R.; Welsh, D. J. A. (eds.), Disorder in Physical Systems: A Volume in Honour of John M. Hammersley. Oxford University Press, 1990.
  6. Stochastic k-tree grammar and its application in biomolecular structure modeling. In Lecture Notes in Computer Science (2014), vol. 8370.
  7. Crystal structure of a hepatitis delta virus ribozyme. Nature 395, 6702 (1998), 567–574.
  8. Durbin, S. E. R. Rna sequence analysis using covariance models. Nucleic Acids Research 22, 11 (1994), 2079–2088.
  9. Eddy, S. R. Hidden markov models. Current opinion in structural biology 6, 3 (1996), 361–365.
  10. Eddy, S. R. Profile hidden markov models. Bioinformatics Review 14, 9 (July 1998), 755–763.
  11. Recent advances in rna folding. Journal of Biotechnology 261 (2017), 97–104.
  12. A single-stranded architecture for cotranscriptional folding of rna nanostructures. Science 345, 6198 (2014), 799–804.
  13. Grimmett, G. R. A theorem about random fields. Bulletin of the London Mathematical Society 5, 1 (1973), 81–84.
  14. The vienna rna websuite. Nucleic Acids Research (2008).
  15. Rna secondary structure prediction with pseudoknots: Contribution of algorithm versus energy model. PLoS One 13, 4 (2018).
  16. A formalism for dependency grammar based on tree adjoining grammar. In Proceedings of the Conference on Meaning-Text Theory (2003).
  17. How rna folds. Journal of Molecular Biology 293, 2 (1999), 271–281.
  18. Koski, T. Hidden Markov Models for Bioinformatics Volume 2. Springer Science and Business Media, New York, New York, 2001.
  19. The estimation of stochastic context-free grammars using the inside-outside algorithm. Computer Speech and Language 4, 1 (1990), 35–56.
  20. Rna pseudoknot prediction in energy-based models. Journal of Computational Biology 7, 3-4 (2000), 409–427.
  21. Maruvada, S. 3-d hand gesture recognition with different temporal behaviors using hmm and kinect. Master’s thesis, University of Magdeburg, Germany, 2017.
  22. Rna puzzles round iii: 3d rna structure prediction of five riboswitches and one ribozyme. RNA 23, 5 (2017), 655 672.
  23. Rna puzzles round iv: 3d structure predictions of four ribozymes and two aptamers. RNA 26, 8 (2020), 982–995.
  24. Rna puzzles round ii: assessment of rna structure prediction programs applied to three large rna structures. RNA 21, 6 (2015), 1066–1084.
  25. Fast algorithm for predicting the secondary structure of single-stranded rna. Proceedings of National Academy of Science 77, 11 (1980), 6309–6313.
  26. An introduction to hidden markov models. IEEE ASSP Magazine 3, 1 (1986), 4–16.
  27. Explainable deep learning: A field guide for the uninitiated. arXiv:2004.14545 (2021).
  28. A range of complex probabilistic models for rna secondary structure prediction that includes the nearest-neighbor model and more. RNA 18, 2 (2011), 193–212.
  29. Stochastic context-free grammars for trna modeling. Nucleic Acids Research 22, 23 (1994), 5112–5120.
  30. Prediction of rna secondary structure including pseudoknots for long sequences. Briefings in Bioinformatics 23, 1 (2022).
  31. Limitations of hidden markov models for the reconstruction of the stacking sequences in close-packed structures. Revista Cubana de Física 34, 1 (2017), 27–31.
  32. Probabilistic independence networks for hidden markov probability models. Neural Computation 9, 2 (1997), 227–269.
  33. Srinivas, S. A generalization of the noisy-or model. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (1993).
  34. Deep learning models for rna secondary structure prediction (probably) do not generalize across families. Bioinformatics 38, 16 (2022), 3892–3899.
  35. Vogue: A variable order hidden markov model with duration based on frequent sequence mining. ACM Transactions on Knowledge Discovery from Data (TKDD) 4, 1 (2010), 1–31.
  36. Review of machine learning methods for rna secondary structure prediction. PLOS Computational Biology (August 2021).
  37. Fr3d: finding local and composite recurrent structural motifs in rna 3d structures. Journal of mathematical Biology 56, 1-2 (2008), 215–252.
  38. Zuker, M. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Research 31, 13 (2003), 3406–3415.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now